Bayesian methods in structural bioinformatics

This book is an edited volume, the goal of which is to provide an overview ofthe current state-of-the-art in statistical methods applied to problems in structural bioinformatics (and in particular protein structure prediction, simulation, experimental structure determination and analysis). It focuses on statistical methods that have a clear interpretation in the framework of statistical physics, rather than ad hoc, black box methods based on neural networks or support vector machines. In addition, the emphasis is on methods that deal withbiomolecular structure in atomic detail. The book is highly accessible, and only assumes background knowledge on protein structure, with a minimum of mathematical knowledge. Therefore, the book includes introductory chapters that contain a solid introduction to key topics such as Bayesian statistics and concepts in machine learning and statistical physics. First book on Bayesian methods in structural bioinformatics, defining an important emerging field. High profile contributors. Unlike other edited volumes, the book forms a solid unity, with nearly 100 pages introductory material. Provides a complete 'starter kit' to the field -Suitable for teaching. INDICE: Part I Foundations: An Overview of Bayesian Inference and Graphical Models. Monte Carlo Methods for Inferences in High-dimensional Systems. PartII Energy Functions for Protein Structure Prediction: On the Physical Relevance and Statistical Interpretation of Knowledge based Potentials. Statistical Machine Learning of Protein Energetics from Experimentally Observed Structures.A Statistical View on the Reference Ratio Method. Part III Directional Statistics and Shape Theory: Statistical Modelling and Simulation Using the Fisher-Bingham Distribution. Statistics of Bivariate von Mises Distributions. BayesianHierarchical Alignment Methods. Likelihood and Empirical Bayes Superpositionsof Multiple Macromolecular Structures. Part IV Graphical models for structureprediction: Probabilistic Models of Local Biomolecular Structure and their Application in Structural Simulation. Prediction of Low Energy Protein Side Chain Configurations Using Markov Random Fields. Part V Inferring Structure from Experimental Data. Inferential Structure Determination from NMR Data. Bayesian Methods in SAXS and SANS Structure Determination.

• ISBN: 978-3-642-27224-0
• Editorial: Springer Berlin Heidelberg
• Encuadernacion: Cartoné
• Páginas: 390
• Fecha Publicación: 31/03/2012
• Nº Volúmenes: 1
• Idioma: Inglés